Let’s first understand what is time series, it is collection of data which varies with time, may be increasing or decreasing with respect to time.A Time series is a series of data points indexed (or listed or graphed) in time order i.e. it is a sequence of discrete time data. Let’s look into example price of stock which may increase or decrease with time, sales of blanket, ice cream is dependent on certain seasons, these kind data which are related to time is consider as aTime Seriesdata.

But why there is a need for this Time Series Analysis…

Model building for problem is done, then what’s next? How to say our model is generalized? How accurate our classification model is? Is our model performing well? Let’s find answer for questions.

We can measure our model performance with the help of **classification metrics** like :

- ACCURACY
- RECALL OR SENSITIVITY
- PRECISION
- F1 SCORE
- AUC
- RUC

Before moving further we can recall about **CONFUSION MATRIX**

If actual and predicted are positive then it belongs to **TRUE POSITIVE **and if the predicted and actual both are negative the it belongs to **TRUE NEGATIVE, **if result predicted is negative but actually it is…

Hello Peers, In This Article Let’s Understand about Linear Regression. This is the Most Basic Algorithm to step into Data Science. As We Know That The ML algorithm is Divided into Supervised, Unsupervised, Semi-Supervised Learning. Linear Regression is one of the Supervised Approach. It Helps to Predicts Independent Feature.

**Simple linear regression** is used to estimate the relationship between** **one dependent and an independent variable.

This is Simple Linear Regression Model, Where Black Points is Actual Data, Blue Line is Predicted Regression Line. **Okay, How this Line gets Formed?** Let’s see ..More generally, a linear model makes a prediction by…

You might have heard about saying “**Birds** of a same **feather** flock together” and “Tell me who your **neighbors** are, I will tell you who you are”. This is the case of KNN where the new data points are classified based on their neighbors.

KNN is used both for regression and classification type of problems. For classification it classifies the new data points based on K-nearest value of their neighbors. For regression type of problems the mean of k-nearest neighbor’s value is taken. But KNN is mostly used for classification type of problems.

**Let’s Go Deeper into the Understanding of…**

Machine Learning and Deep Learning Enthusiast, Currently Pursuing MSC Data Science.